Instruction-based language modeling has received significant attention in pretrained language models. However, the efficiency of instruction engineering remains low and hinders the development of instruction studies. Recent studies have focused on automating instruction generation, but they primarily aim to improve performance without considering other crucial objectives that impact instruction quality, such as instruction length and perplexity. Therefore, we propose a novel approach (i.e., InstOptima) that treats instruction generation as an evolutionary multi-objective optimization problem. In contrast to text edition-based methods, our approach utilizes a large language model (LLM) to simulate instruction operators, including mutation and crossover. Furthermore, we introduce an objective-guided mechanism for these operators, allowing the LLM to comprehend the objectives and enhance the quality of the generated instructions. Experimental results demonstrate improved fine-tuning performance and the generation of a diverse set of high-quality instructions.

翻译：基于指令的语言建模在预训练语言模型中受到了广泛关注。然而，指令工程效率低下，阻碍了指令研究的发展。近期研究聚焦于自动化指令生成，但主要旨在提升性能，而忽略了影响指令质量的其他关键目标（如指令长度与困惑度）。为此，我们提出一种新方法（即InstOptima），将指令生成视为进化多目标优化问题。与基于文本编辑的方法不同，我们的方法利用大语言模型（LLM）模拟指令算子（包括变异与交叉）。此外，我们引入了一种面向目标的引导机制，使LLM能够理解目标并提升生成指令的质量。实验结果表明，该方法改进了微调性能，并生成了一组多样化且高质量的指令。